Diffusion models [1]}, [2]} are a class of deep generative models which learn a data distribution by reversing a scheduled noising process.
In the past few years, diffusion models have been shown to be a promising avenue for generative modeling, exceeding the state-of-the-art in image and video generation tasks [3]}, [4]}.
Much like previous generative approaches like VAE [5]} and GAN [6]}, diffusion models are also capable of conditional generation.
Dhariwal et al. [7]} introduced classifier guidance for image generation, where the output of a diffusion model may be “steered" towards a target, such as a class label, using the gradients of a differentiable auxiliary model.
Saharia et al. [8]} proposed to use direct concatenation of conditions for Pix2Pix-like tasks, akin to Conditional GAN and CVAE[9]}, [10]}, and Ho et al. [11]} demonstrated that classifier-free guidance can achieve state-of-the-art results while allowing more explicit control over the diversity-fidelity tradeoff.
